Abstract
Purpose: Vacuum-assisted breast biopsy (VABB), widely used for diagnosing
breast cancer, is prone to sampling errors that may necessitate repeat
procedures and delay treatment. Optically guided VABB using Diffuse
Reflectance Spectroscopy (DRS) may help address this limitation.
Methods: To this end, an optical introducer (a cut-out 9-gauge hollow tube) was
developed and equipped with optical fibers. The tube slides over a standard
VABB needle, enabling tis- sue measurements near the biopsy aperture. Ex vivo DRS measurements were performed on lumpectomy specimens from 90 patients using this tube, yielding over 500 measurement locations. Tissue labels were derived through deformable image registration of measurement sites with annotated histology, and label extraction was optimized for larger fiber distances. Machine learning classifiers were trained on the spectral data.
Results: Ensemble models achieved up to 94% sensitivity at a 20% tumor
threshold and maintained high performance at lower thresholds.
Conclusion: These findings demonstrate that DRS enables real-time tissue
characterization at large fiber distances and may improve biopsy precision. In vivo studies are needed to validate performance and support the integration of DRS into routine VABB diagnostic workflows.
breast cancer, is prone to sampling errors that may necessitate repeat
procedures and delay treatment. Optically guided VABB using Diffuse
Reflectance Spectroscopy (DRS) may help address this limitation.
Methods: To this end, an optical introducer (a cut-out 9-gauge hollow tube) was
developed and equipped with optical fibers. The tube slides over a standard
VABB needle, enabling tis- sue measurements near the biopsy aperture. Ex vivo DRS measurements were performed on lumpectomy specimens from 90 patients using this tube, yielding over 500 measurement locations. Tissue labels were derived through deformable image registration of measurement sites with annotated histology, and label extraction was optimized for larger fiber distances. Machine learning classifiers were trained on the spectral data.
Results: Ensemble models achieved up to 94% sensitivity at a 20% tumor
threshold and maintained high performance at lower thresholds.
Conclusion: These findings demonstrate that DRS enables real-time tissue
characterization at large fiber distances and may improve biopsy precision. In vivo studies are needed to validate performance and support the integration of DRS into routine VABB diagnostic workflows.
| Original language | English |
|---|---|
| Number of pages | 44 |
| Journal | Journal of translational medicine |
| DOIs | |
| Publication status | E-pub ahead of print/First online - 20 Dec 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Vacuum-assisted breast biopsy
- Diffuse reflectance spectroscopy
- Optical guidance
- Breast cancer diagnosis
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